So far we’ve encountered two ways of writing values: expression statements and
the print statement. (A third way is using the write() method
of file objects; the standard output file can be referenced as sys.stdout.
See the Library Reference for more information on this.)

Often you’ll want more control over the formatting of your output than simply
printing space-separated values. There are two ways to format your output; the
first way is to do all the string handling yourself; using string slicing and
concatenation operations you can create any layout you can imagine. The
string types have some methods that perform useful operations for padding
strings to a given column width; these will be discussed shortly. The second
way is to use the str.format() method.

The string module contains a Template class which offers
yet another way to substitute values into strings.

One question remains, of course: how do you convert values to strings? Luckily,
Python has ways to convert any value to a string: pass it to the repr()
or str() functions.

The str() function is meant to return representations of values which are
fairly human-readable, while repr() is meant to generate representations
which can be read by the interpreter (or will force a SyntaxError if
there is no equivalent syntax). For objects which don’t have a particular
representation for human consumption, str() will return the same value as
repr(). Many values, such as numbers or structures like lists and
dictionaries, have the same representation using either function. Strings and
floating point numbers, in particular, have two distinct representations.

(Note that in the first example, one space between each column was added by the
way print works: by default it adds spaces between its arguments.)

This example demonstrates the str.rjust() method of string
objects, which right-justifies a string in a field of a given width by padding
it with spaces on the left. There are similar methods str.ljust() and
str.center(). These methods do not write anything, they just return a
new string. If the input string is too long, they don’t truncate it, but
return it unchanged; this will mess up your column lay-out but that’s usually
better than the alternative, which would be lying about a value. (If you
really want truncation you can always add a slice operation, as in
x.ljust(n)[:n].)

There is another method, str.zfill(), which pads a numeric string on the
left with zeros. It understands about plus and minus signs:

>>> print'We are the {} who say "{}!"'.format('knights','Ni')We are the knights who say "Ni!"

The brackets and characters within them (called format fields) are replaced with
the objects passed into the str.format() method. A number in the
brackets refers to the position of the object passed into the
str.format() method.

>>> print'{0} and {1}'.format('spam','eggs')spam and eggs>>> print'{1} and {0}'.format('spam','eggs')eggs and spam

If keyword arguments are used in the str.format() method, their values
are referred to by using the name of the argument.

>>> print'The story of {0}, {1}, and {other}.'.format('Bill','Manfred',... other='Georg')The story of Bill, Manfred, and Georg.

'!s' (apply str()) and '!r' (apply repr()) can be used to
convert the value before it is formatted.

>>> importmath>>> print'The value of PI is approximately {}.'.format(math.pi)The value of PI is approximately 3.14159265359.>>> print'The value of PI is approximately {!r}.'.format(math.pi)The value of PI is approximately 3.141592653589793.

An optional ':' and format specifier can follow the field name. This allows
greater control over how the value is formatted. The following example
rounds Pi to three places after the decimal.

>>> importmath>>> print'The value of PI is approximately {0:.3f}.'.format(math.pi)The value of PI is approximately 3.142.

Passing an integer after the ':' will cause that field to be a minimum
number of characters wide. This is useful for making tables pretty.

If you have a really long format string that you don’t want to split up, it
would be nice if you could reference the variables to be formatted by name
instead of by position. This can be done by simply passing the dict and using
square brackets '[]' to access the keys

The % operator can also be used for string formatting. It interprets the
left argument much like a sprintf()-style format string to be applied
to the right argument, and returns the string resulting from this formatting
operation. For example:

>>> importmath>>> print'The value of PI is approximately %5.3f.'%math.piThe value of PI is approximately 3.142.

The first argument is a string containing the filename. The second argument is
another string containing a few characters describing the way in which the file
will be used. mode can be 'r' when the file will only be read, 'w'
for only writing (an existing file with the same name will be erased), and
'a' opens the file for appending; any data written to the file is
automatically added to the end. 'r+' opens the file for both reading and
writing. The mode argument is optional; 'r' will be assumed if it’s
omitted.

On Windows, 'b' appended to the mode opens the file in binary mode, so there
are also modes like 'rb', 'wb', and 'r+b'. Python on Windows makes
a distinction between text and binary files; the end-of-line characters in text
files are automatically altered slightly when data is read or written. This
behind-the-scenes modification to file data is fine for ASCII text files, but
it’ll corrupt binary data like that in JPEG or EXE files. Be
very careful to use binary mode when reading and writing such files. On Unix,
it doesn’t hurt to append a 'b' to the mode, so you can use it
platform-independently for all binary files.

The rest of the examples in this section will assume that a file object called
f has already been created.

To read a file’s contents, call f.read(size), which reads some quantity of
data and returns it as a string. size is an optional numeric argument. When
size is omitted or negative, the entire contents of the file will be read and
returned; it’s your problem if the file is twice as large as your machine’s
memory. Otherwise, at most size bytes are read and returned. If the end of
the file has been reached, f.read() will return an empty string ("").

>>> f.read()'This is the entire file.\n'>>> f.read()''

f.readline() reads a single line from the file; a newline character (\n)
is left at the end of the string, and is only omitted on the last line of the
file if the file doesn’t end in a newline. This makes the return value
unambiguous; if f.readline() returns an empty string, the end of the file
has been reached, while a blank line is represented by '\n', a string
containing only a single newline.

>>> f.readline()'This is the first line of the file.\n'>>> f.readline()'Second line of the file\n'>>> f.readline()''

For reading lines from a file, you can loop over the file object. This is memory
efficient, fast, and leads to simple code:

>>> forlineinf: print line,This is the first line of the file.Second line of the file

If you want to read all the lines of a file in a list you can also use
list(f) or f.readlines().

f.write(string) writes the contents of string to the file, returning
None.

>>> f.write('This is a test\n')

To write something other than a string, it needs to be converted to a string
first:

>>> value=('the answer',42)>>> s=str(value)>>> f.write(s)

f.tell() returns an integer giving the file object’s current position in the
file, measured in bytes from the beginning of the file. To change the file
object’s position, use f.seek(offset,from_what). The position is computed
from adding offset to a reference point; the reference point is selected by
the from_what argument. A from_what value of 0 measures from the beginning
of the file, 1 uses the current file position, and 2 uses the end of the file as
the reference point. from_what can be omitted and defaults to 0, using the
beginning of the file as the reference point.

>>> f=open('workfile','r+')>>> f.write('0123456789abcdef')>>> f.seek(5)# Go to the 6th byte in the file>>> f.read(1)'5'>>> f.seek(-3,2)# Go to the 3rd byte before the end>>> f.read(1)'d'

When you’re done with a file, call f.close() to close it and free up any
system resources taken up by the open file. After calling f.close(),
attempts to use the file object will automatically fail.

It is good practice to use the with keyword when dealing with file
objects. This has the advantage that the file is properly closed after its
suite finishes, even if an exception is raised on the way. It is also much
shorter than writing equivalent try-finally blocks:

Strings can easily be written to and read from a file. Numbers take a bit more
effort, since the read() method only returns strings, which will have to
be passed to a function like int(), which takes a string like '123'
and returns its numeric value 123. When you want to save more complex data
types like nested lists and dictionaries, parsing and serializing by hand
becomes complicated.

Rather than having users constantly writing and debugging code to save
complicated data types to files, Python allows you to use the popular data
interchange format called JSON (JavaScript Object Notation). The standard module called json can take Python
data hierarchies, and convert them to string representations; this process is
called serializing. Reconstructing the data from the string representation
is called deserializing. Between serializing and deserializing, the
string representing the object may have been stored in a file or data, or
sent over a network connection to some distant machine.

Note

The JSON format is commonly used by modern applications to allow for data
exchange. Many programmers are already familiar with it, which makes
it a good choice for interoperability.

If you have an object x, you can view its JSON string representation with a
simple line of code:

Another variant of the dumps() function, called dump(),
simply serializes the object to a file. So if f is a file object
opened for writing, we can do this:

json.dump(x,f)

To decode the object again, if f is a file object which has
been opened for reading:

x=json.load(f)

This simple serialization technique can handle lists and dictionaries, but
serializing arbitrary class instances in JSON requires a bit of extra effort.
The reference for the json module contains an explanation of this.

Contrary to JSON, pickle is a protocol which allows
the serialization of arbitrarily complex Python objects. As such, it is
specific to Python and cannot be used to communicate with applications
written in other languages. It is also insecure by default:
deserializing pickle data coming from an untrusted source can execute
arbitrary code, if the data was crafted by a skilled attacker.